Analytical and Informatic Benefits from Automated Conversion of Ion Mobility Arrival Times to Collision Cross Sections in Raw Data Files

Importance of the Topic

Faster and more selective separations are essential for high-throughput mass spectrometry workflows, especially in fields such as metabolomics and lipidomics.
Ion mobility spectrometry (IM) provides an orthogonal gas-phase separation based on ion shape and size, but raw arrival time data vary with instrumental and method parameters.
Automated conversion of arrival times to collision cross section (CCS) values within raw data files offers a calibration-free standardization strategy that improves reproducibility, comparability and downstream informatics.

Study Objectives and Overview

This work evaluates the analytical and informatic benefits of integrating automated IM arrival time to CCS conversion directly into raw IM-MS data files.
Using a prototype high-resolution IM device (SLIM-HRIM) coupled to LC-QTOF, the study examines reproducibility of retention time, arrival time and CCS under varying LC gradients, flow rates and SLIM wave conditions.
Comparisons are made between feature-based CCS centroids and pixel-wise CCS mapping, as well as with single-field CCS values from a drift tube IM instrument.

Methodology and Instrumentation

Experiments were performed on:

• A SLIM-based high-resolution IM device (MOBILion Systems) coupled to an Agilent 6546 LC/Q-TOF
• An Agilent 1290 Infinity II LC system with HILIC column (3.0×100 mm, 1.8 μm) for lipid analysis
• Agilent Tune Mix and Avanti Polar lipid standards for calibration and performance evaluation

Data processing steps included:

• Use of PNNL PreProcessor to map individual (m/z, arrival time) points to (m/z, CCS)
• Drift bin summing, smoothing, thresholding and spike removal to improve data quality
• Feature finding and CCS calibration in Agilent IM-MS Browser

Main Results and Discussion

• Retention time shifts across LC gradients and flow rates reached 12–13.5%, and arrival time shifts across SLIM wave settings reached ~26–30%
• Mapping to CCS space reduced variation to below 0.6% across all conditions and within-method reproducibility to <0.05% between feature-based and mapped CCS values
• CCS differences across three SLIM wave settings remained below 0.54%, demonstrating consistency of automated mapping
• SLIM-derived CCS values agreed with Agilent 6560 drift tube single-field CCS values within 2% for both positive and negative lipid ions

Benefits and Practical Applications

• Standardizes IM measurements for reliable cross-platform and cross-laboratory comparisons
• Enhances reproducibility and confidence in complex mixture analyses such as lipidomics
• Facilitates creation and sharing of CCS libraries for compound identification
• Reduces downstream processing time and data storage requirements through preprocessing

Future Trends and Opportunities

Integration of automated CCS mapping into commercial vendor and open-source software will streamline analytical pipelines.
Expansion of community CCS libraries covering diverse compound classes will support untargeted omics studies.
Combining CCS data with machine learning approaches may unlock new insights into molecular conformations and structural isomers.

Conclusion

Automated conversion of IM arrival times to CCS within raw data files offers a robust calibration strategy that improves reproducibility and comparability across variable experimental conditions.
This approach unifies IM-MS results, supports reliable library generation and enhances informatics workflows, paving the way for broader adoption of CCS as a molecular descriptor.

Reference

1. May JC, et al. Resolving Power and Collision Cross Section Measurement Accuracy of a Prototype High-Resolution Ion Mobility Platform Incorporating Structures for Lossless Ion Manipulation. J Am Soc Mass Spectrom. 2021;32(4):1126–1137.
2. Bilbao A, et al. A Preprocessing Tool for Enhanced Ion Mobility–Mass Spectrometry-Based Omics Workflows. J Proteome Res. 2021.